Abstract

In the last few decades, geospatial simulations have become increasingly common in scientific research as a method to study complex spatial phenomena, such as urban growth or disease spread, and to effectively prevent or mitigate natural hazards, including flooding or wildfires. Although exploring simulated scenarios can help us predict future demands and risks associated with decisions and policies, geospatial simulations are often not designed for practical use in management. The black box nature and the lack of user-friendly interfaces of simulation tools make them inaccessible for decision makers and stakeholders, leading to a knowledge-practice gap. To address this challenge, we developed a decision support system on top of Tangible Landscape, an open source tangible geospatial modeling platform. By coupling a physical, scaled model of a landscape with powerful geospatial modeling platform, Tangible Landscape allows decision-makers to intuitively modify landscape and perform spatial interventions while instantly visualizing and quantifying the resulting effects. Tangible, spatio-temporal steering of the simulation enhances understanding of simulated processes, communicates uncertainties and builds trust between decision-makers and researchers. As a case study, we are leveraging this novel modeling platform to engage stakeholders involved in the management of a weather-driven plant disease aggressively spreading in California and Oregon. Our results suggest that Tangible Landscape is a promising platform for supporting decisions and building more robust, accurate scientific models.

Abstract

Climate conditions, vegetation, fuel type and availability govern
the occurence and behavior of fires. Understanding the seasonality,
frequency, intensity, and severity of fires is critical for land managers
to appropriately manage and plan the landscape and to understand the
feedbacks to the Earth system. Fire Regimes are conceptually useful to
land managers and are qualitatively understood, but few quantitative
techniques exist for empirically delineating geographic regions whose
wildfire spatial and temporal characteristics, re-visitation frequency,
and intensities are similar.

We consider the extensive and consistent thermal “hotspot” data
which are collected globally by the two MODIS sensors during their
17-year orbital history. Such ubiquitous remote sensing data provide
an opportunity to produce a quantitative discrimination of different
global fire regimes, including tele-connections across hemispheres. We
do not filter or remove human-caused fires from wildfires, instead
considering and classifying both types of fire regimes holistically. To
appropriately address opposing seasonal juxtaposition across northern
and southern hemispheres we developed a special transformation of fire
dates which allows statistical identification and discrimination of,
say, “summer” fires, regardless of the calendar month in which
they occurred across the hemispheres. This date transform permits
the recognition of similar fire regimes in both the northern and
southern hemispheres. On the basis of about twenty descriptive fire
characteristics, we produced a series of global maps at multiple levels
of fire regime discrimination. By applying principal component analysis,
we also visually quantify the degree of similarity among the different
global fire regimes and quantitatively identify the characteristics
responsible for the similarities or differences.

Geographically distant locations which share similar fire regime
characteristics were found; many of these fire “tele-connections”
span across different hemispheres. Regularly occurring human-caused
Fire Regimes can also be easily identified globally. Locations sharing
similar global fire regimes may have similar ecological effects and
impacts from fire, and similar management knowledge and successful
adaptation strategies might be borrowed, shared, or adopted.